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| Perhatian Kendiri Pelbagai Kepala× | Fine-Tuning BERT× | Penalaan Halus GPT× | |
|---|---|---|---|
| Bidang | Pembelajaran Mendalam | Pembelajaran Mendalam | Pembelajaran Mendalam |
| Keluarga | Machine learning | Machine learning | Machine learning |
| Tahun asal≠ | 2017 | 2019 | 2019 |
| Pengasas≠ | Vaswani, A. et al. | Devlin, J. et al. | Radford, A. et al. (OpenAI) |
| Jenis≠ | Attention mechanism (Transformer core) | Transfer learning (fine-tuning a pre-trained transformer) | Fine-tuning of pretrained autoregressive language models |
| Sumber perintis≠ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. DOI ↗ | Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report. link ↗ |
| Alias | Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention | BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning |
| Berkaitan | 5 | 5 | 5 |
| Ringkasan≠ | Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5. | BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data. | GPT fine-tuning adapts pretrained autoregressive language models such as GPT-2/3/4 or LLaMA — introduced in OpenAI's 2019 work by Radford and colleagues — to domain-specific data or to instruction following via reinforcement learning from human feedback (RLHF) or DPO. It is used for instruction following, domain adaptation, and generative tasks. |
| ScholarGateSet data ↗ |
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